2 research outputs found
Minimal model of associative learning for cross-situational lexicon acquisition
An explanation for the acquisition of word-object mappings is the associative
learning in a cross-situational scenario. Here we present analytical results of
the performance of a simple associative learning algorithm for acquiring a
one-to-one mapping between objects and words based solely on the
co-occurrence between objects and words. In particular, a learning trial in our
learning scenario consists of the presentation of objects together
with a target word, which refers to one of the objects in the context. We find
that the learning times are distributed exponentially and the learning rates
are given by in the case the target
words are sampled randomly and by in the
case they follow a deterministic presentation sequence. This learning
performance is much superior to those exhibited by humans and more realistic
learning algorithms in cross-situational experiments. We show that introduction
of discrimination limitations using Weber's law and forgetting reduce the
performance of the associative algorithm to the human level